IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-024-02786-z.html
   My bibliography  Save this article

Mapping the evolution of algorithmic HRM (AHRM): a multidisciplinary synthesis

Author

Listed:
  • Roslyn Cameron

    (Torrens University Australia)

  • Heinz Herrmann

    (Torrens University Australia)

  • Alan Nankervis

    (Curtin University)

Abstract

High levels of confusion persist around the term “algorithm” in general; and in addition to this, there is also conceptual confusion around the application of algorithms to human resource management (HRM) strategy and functions. Although there are several systematic reviews of various algorithmic applications to HRM and many of its functions, no comprehensive evolutionary map of the emergent field of algorithmic HRM (AHRM) could be found in the academic literature. This study has dual aims. The first is to provide conceptual clarity for the field of AHRM, and the second is to map the evolution of AHRM from 2000 to 2022. To address the first aim, we conduct a multidisciplinary synthesis of the concepts related to algorithms which results in a General Framework for Algorithmic Decision-Making. This framework then informs the empirical part of the study which addresses the second aim. A science mapping review is employed to chart and assess the extant literature on algorithmic HRM from 2000 to 2022. This study presents a General Framework for Algorithmic Decision-Making across all business functions and then a Framework for Algorithmic AHRM Tools. This provides conceptual clarity and distinguishes between automated and augmented HR decision-making. Findings also reveal the multidisciplinary nature of this emergent field of inquiry and point to current research, which focuses on specialized applications for HR functions such as workforce planning, learning and development, allocation and scheduling, and recruitment; but lacks emphasis on more integrative strategic HRM contexts. The study also has implications for organizational strategic decision-making. HR practitioners may need to form project teams with their information technology (IT) and data analyst colleagues when making strategic decisions about algorithmic applications for HR strategy and HR functions. This also lends itself to future research with multidisciplinary research teams including HR researchers along with computer scientists, computational engineers, and data analysts.

Suggested Citation

  • Roslyn Cameron & Heinz Herrmann & Alan Nankervis, 2024. "Mapping the evolution of algorithmic HRM (AHRM): a multidisciplinary synthesis," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02786-z
    DOI: 10.1057/s41599-024-02786-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-02786-z
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-024-02786-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    2. Syaiful Anwar Mohamed & Moamin A. Mahmoud & Mohammed Najah Mahdi & Salama A. Mostafa, 2022. "Improving Efficiency and Effectiveness of Robotic Process Automation in Human Resource Management," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    3. Kibaek Kim & Sanjay Mehrotra, 2015. "A Two-Stage Stochastic Integer Programming Approach to Integrated Staffing and Scheduling with Application to Nurse Management," Operations Research, INFORMS, vol. 63(6), pages 1431-1451, December.
    4. Sheshadri Chatterjee & Ranjan Chaudhuri & Demetris Vrontis & Evangelia Siachou, 2021. "Examining the dark side of human resource analytics: an empirical investigation using the privacy calculus approach," International Journal of Manpower, Emerald Group Publishing Limited, vol. 43(1), pages 52-74, June.
    5. Martín-Martín, Alberto & Orduna-Malea, Enrique & Thelwall, Mike & Delgado López-Cózar, Emilio, 2018. "Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories," Journal of Informetrics, Elsevier, vol. 12(4), pages 1160-1177.
    6. Pitt, Christine S. & Botha, Elsamari & Ferreira, João J. & Kietzmann, Jan, 2018. "Employee brand engagement on social media: Managing optimism and commonality," Business Horizons, Elsevier, vol. 61(4), pages 635-642.
    7. Eva A. M. van Dis & Johan Bollen & Willem Zuidema & Robert van Rooij & Claudi L. Bockting, 2023. "ChatGPT: five priorities for research," Nature, Nature, vol. 614(7947), pages 224-226, February.
    8. Franceschini, Fiorenzo & Maisano, Domenico & Mastrogiacomo, Luca, 2016. "The museum of errors/horrors in Scopus," Journal of Informetrics, Elsevier, vol. 10(1), pages 174-182.
    9. Nada Al Mehrzi & Sanjay Kumar Singh, 2016. "Competing through employee engagement: a proposed framework," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 65(6), pages 831-843, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ming-yueh Tsay & Yu-wei Tseng & Tai-luan Wu, 2019. "Comprehensiveness and uniqueness of commercial databases and open access systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1323-1338, December.
    2. Iva Gregurec & Martina Tomičić Furjan & Katarina Tomičić-Pupek, 2021. "The Impact of COVID-19 on Sustainable Business Models in SMEs," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    3. David Jancsics & Salvador Espinosa & Jonathan Carlos, 2023. "Organizational noncompliance: an interdisciplinary review of social and organizational factors," Management Review Quarterly, Springer, vol. 73(3), pages 1273-1301, September.
    4. Ghio, Alessandro, 2024. "Democratizing academic research with Artificial Intelligence: The misleading case of language," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 98(C).
    5. Jingqi Gao & Xiang Wu & Xiaowei Luo & Shukai Guan, 2021. "Scientometric Analysis of Safety Sign Research: 1990–2019," IJERPH, MDPI, vol. 18(1), pages 1-15, January.
    6. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    7. Cristina Robledo-Ardila & Juan Pablo Román-Calderón, 2022. "Potential: in search for meaning, theory and avenues for future research a systematic review," Management Review Quarterly, Springer, vol. 72(1), pages 149-186, February.
    8. Xiaowei Yang & Shumin Yan & Jiang He & Junjie Dong, 2022. "Review and Prospects of Enterprise Human Resource Management Effectiveness: Bibliometric Analysis Based on Chinese-Language and English-Language Journals," Sustainability, MDPI, vol. 14(23), pages 1-16, December.
    9. Sandeep Rath & Kumar Rajaram, 2022. "Staff Planning for Hospitals with Implicit Cost Estimation and Stochastic Optimization," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1271-1289, March.
    10. Vivek Kumar Singh & Prashasti Singh & Mousumi Karmakar & Jacqueline Leta & Philipp Mayr, 2021. "The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5113-5142, June.
    11. Evangelos Katsamakas & Oleg V. Pavlov & Ryan Saklad, 2024. "Artificial intelligence and the transformation of higher education institutions," Papers 2402.08143, arXiv.org.
    12. Gonzalo Wandosell & María C. Parra-Meroño & Alfredo Alcayde & Raúl Baños, 2021. "Green Packaging from Consumer and Business Perspectives," Sustainability, MDPI, vol. 13(3), pages 1-19, January.
    13. Mikhail Rogov & Céline Rozenblat, 2018. "Urban Resilience Discourse Analysis: Towards a Multi-Level Approach to Cities," Sustainability, MDPI, vol. 10(12), pages 1-21, November.
    14. Pantea Kamrani & Isabelle Dorsch & Wolfgang G. Stock, 2021. "Do researchers know what the h-index is? And how do they estimate its importance?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5489-5508, July.
    15. Perez-Vega, Rodrigo & Hopkinson, Paul & Singhal, Aishwarya & Mariani, Marcello M., 2022. "From CRM to social CRM: A bibliometric review and research agenda for consumer research," Journal of Business Research, Elsevier, vol. 151(C), pages 1-16.
    16. Weishu Liu & Meiting Huang & Haifeng Wang, 2021. "Same journal but different numbers of published records indexed in Scopus and Web of Science Core Collection: causes, consequences, and solutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4541-4550, May.
    17. Junshuai Cheng & Qaisar Iqbal & Guangmeng Ji & Weichun Li, 2022. "A Sustainable and Comprehensive Framework for Knowledge Transfer in MNCs: An Empirical Examination Based on Country, Company and Individual Levels of Chinese MNCs," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    18. Zoltán Krajcsák, 2021. "Researcher Performance in Scopus Articles ( RPSA ) as a New Scientometric Model of Scientific Output: Tested in Business Area of V4 Countries," Publications, MDPI, vol. 9(4), pages 1-23, October.
    19. Tianlong Yu & Hao Yang & Xiaowei Luo & Yifeng Jiang & Xiang Wu & Jingqi Gao, 2021. "Scientometric Analysis of Disaster Risk Perception: 2000–2020," IJERPH, MDPI, vol. 18(24), pages 1-19, December.
    20. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02786-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.